Franklin Open最新文献

{"title":"Perturbation based privacy preservation and classification using Jaya Algorithm and Dragonfly Inspired Algorithm","authors":"Dipanwita Sen,&nbsp;Bhupati Bhusan Mishra,&nbsp;Prasant Kumar Pattnaik","doi":"10.1016/j.fraope.2025.100266","DOIUrl":"10.1016/j.fraope.2025.100266","url":null,"abstract":"<div><div>Healthcare datasets are very sensitive datasets. In case of unauthorized access, sensitive datasets could potentially cause damage, discrimination and unsolicited scrutiny. The patients’ health details constitute private personal information. They should not be disclosed. However, data might get stored in cloud without any protection. That is why, privacy preservation of data for healthcare dataset is a significant consideration. In this work, the Wisconsin Prognostic Breast Cancer (WBC) dataset has been used. At first, a privacy preserving schema making use of perturbation implementing Jaya Algorithm has been elucidated. Out of 30 numerical attributes in the dataset, 6 are chosen for perturbation based on their relatively high Pearson’s Correlation coefficient values. They form the initial population of Jaya Algorithm. The objective function is defined and we opt for a minimization problem. After each iteration, the algorithm generates a new population from the previous population. Thereafter, the accuracies obtained by a few traditional classification algorithms as well as classifiers based on some meta-heuristic algorithms, are observed . The classical classifiers used are Decision Tree, Random Forest, AdaBoost, KNN and GNB. The standard evaluation metrics are recorded thereafter. For privacy, the evaluation metrics used are Secrecy, Value Difference(VD), RP, RK, CP and CK. For utility, the metrics are Accuracy, Precision, Recall, F1-Score and Area Under the Curve(AUC). Jaya Algorithm is then compared with traditional perturbation algorithms like 2DRT and 3DRT. It is seen that Jaya preserves more privacy and retains more utility as suggested by mean Friedman Test Rankings.. Among the metaheuristic optimization based classifiers, only the Dragonfly inspired Classifier(DIC) classifies over 90 % of the records correctly for the perturbed dataset. For classification, the perturbed dataset is fed into the DIC as the original population. The target is to minimize the distance between the testing dataset points and the centroids assigned to them. The new centroids are calculated using the updated training set points only. The updated dragonflies are assigned new centroids at each stage. All these simulations have been implemented in Python environment.</div></div>","PeriodicalId":100554,"journal":{"name":"Franklin Open","volume":"11 ","pages":"Article 100266"},"PeriodicalIF":0.0,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143900277","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Clustering digital mental health perceptions using transformer-based models","authors":"Ayodeji O.J. Ibitoye ,&nbsp;Oladosu O. Oladimeji ,&nbsp;Oluwaseyi F. Afe","doi":"10.1016/j.fraope.2025.100262","DOIUrl":"10.1016/j.fraope.2025.100262","url":null,"abstract":"<div><div>The rise in online mental health discussions underscores the need to understand diverse perspectives to inform targeted interventions. Addressing the granularity of existing research on mapping such perspectives, this study proposes a model combining contextual and sentiment analysis, keyword scoring, and clustering techniques to identify themes in online comments. Using transformer-based models (BERT, ALBERT, ELECTRA), the study achieved high-quality clustering of seven distinct mental health perspectives: stigma, empowerment, treatment approaches, recovery, social/environmental factors, advocacy, and cultural dimensions. ELECTRA outperformed others in clustering quality (silhouette score: 0.73; Davies-Bouldin Index: 0.34). The findings reveal cohesive, well-separated clusters that enhance understanding of digital mental health discourse. These insights provide a foundation for data-driven advocacy, tailored interventions, and broader awareness, addressing the complex dynamics of mental health narratives in online spaces. This study bridges a critical research gap by offering a systematic approach to analysing and interpreting mental health perspectives in digital environments.</div></div>","PeriodicalId":100554,"journal":{"name":"Franklin Open","volume":"11 ","pages":"Article 100262"},"PeriodicalIF":0.0,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143869619","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"COLD-12: A multi-level feature extraction hybrid CNN Model for accurate cotton disease diagnosis","authors":"Md. Asraful Sharker Nirob,&nbsp;Prayma Bishshash,&nbsp;A K M Fazlul Kobir Siam","doi":"10.1016/j.fraope.2025.100263","DOIUrl":"10.1016/j.fraope.2025.100263","url":null,"abstract":"<div><div>Cotton leaf diseases are common agricultural issues and multifaceted threats to millions of farmers worldwide. This study aims to provide an up-to-date approach to diagnosing diseases of cotton leaves using advanced DL (Deep Learning) that is accessible and actionable by farmers, bridging the gap between technology and agricultural practices. The key to this study was the construction of a diverse dataset comprising 5722 high-resolution images collected from three independent sources and further enriched with expert guidance in annotation. Advanced preprocessing was done, like denoising, sharpening, and color balancing, to increase the quality of images, while augmentation increased generalization and reduced overfitting. In it, COLD-12 is proposed, which has achieved a promising result by incorporating Atrous Spatial Pyramid Pooling with Squeeze-and-Excitation blocks for improved channel attention in its multi-level feature extraction. The training accuracy of the model was 99.94 %, and the validation accuracy was 99.24 %, which indicates that the model was robust with very low validation loss. The inner workings of the COLD-12 model were interpreted by using explainable AI techniques such as Grad-CAM, Grad-CAM++, and Layer-CAM. It showed advantages compared to state-of-the-art models such as VGG16, VGG19, Xception, DenseNet121, and InceptionResNetV2.The impact of batch size, K-fold cross-validation, and preprocessing techniques was also explored, resulting in improved accuracy and interpretability. An interactive web tool was developed to bridge research and real-world applications, offering farmers a convenient assistant for diagnosing cotton leaf diseases, thus supporting sustainable farming initiatives.</div></div>","PeriodicalId":100554,"journal":{"name":"Franklin Open","volume":"11 ","pages":"Article 100263"},"PeriodicalIF":0.0,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143873586","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A modified three-term of Dai–Yuan type derivative-free algorithm for nonlinear monotone equations with signal recovery problems","authors":"Aliyu Yusuf ,&nbsp;Nibron Haggai Manjak ,&nbsp;Abubakar Muhammad Kwami ,&nbsp;Mohammed Abdulhameed","doi":"10.1016/j.fraope.2025.100255","DOIUrl":"10.1016/j.fraope.2025.100255","url":null,"abstract":"<div><div>A lot of researchers have introduced iterative schemes for solving convex constrained nonlinear monotone equations. This paper aims to propose a modified three-term of Dai–Yuan type (TTDY) derivative-free algorithm for nonlinear monotone equations. The presented method has a low storage requirement, it can therefore easily solve large scale nonlinear equations. At every iteration, it generates a descent search direction independent of the line search. We established the global convergence of the propose approach under standard conditions. Numerical examples are given to illustrate the effectiveness of the algorithm when solving large-scale nonlinear monotone equations. Finally, the capacity of the proposed algorithm has been tested to solve nonlinear monotone equations equivalent to the <span><math><msub><mrow><mi>ℓ</mi></mrow><mrow><mn>1</mn></mrow></msub></math></span>-norm regularized minimization problem.</div></div>","PeriodicalId":100554,"journal":{"name":"Franklin Open","volume":"11 ","pages":"Article 100255"},"PeriodicalIF":0.0,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143843371","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fault-tolerant Q-learning for discrete-time linear systems with actuator and sensor faults using input-output measured data","authors":"Mohammadrasoul Kankashvar,&nbsp;Sajad Rafiee,&nbsp;Hossein Bolandi","doi":"10.1016/j.fraope.2025.100259","DOIUrl":"10.1016/j.fraope.2025.100259","url":null,"abstract":"<div><div>This study presents a novel output feedback Q-learning algorithm specifically designed for fault-tolerant control in real-time applications, circumventing the necessity for explicit system models or detailed actuator and sensor fault information. A significant benefit of this algorithm is its capability to simultaneously achieve optimality and stabilize systems with both actuator and sensor faults. Unlike traditional methods, it learns online using input-output data from the faulty system, bypassing the need for full-state measurements. We develop a unique expression of the Fault-Tolerant Q-function (FTQF) in the input-output format and derive a model-free optimal output feedback fault-tolerant control (FTC) policy. Furthermore, the algorithm's real-time implementation process is detailed, showing its adaptability in acquiring optimal output feedback FTC policies without prior knowledge of system dynamics or faults. The proposed method remains unaffected by excitation noise bias, even without a discount factor, and guarantees closed-loop stability and convergence to optimal solutions. Validation through numerical simulations on an F-16 autopilot aircraft underscores its effectiveness.</div></div>","PeriodicalId":100554,"journal":{"name":"Franklin Open","volume":"11 ","pages":"Article 100259"},"PeriodicalIF":0.0,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143848249","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mathematical Modelling and Analysis of Lassa fever Dynamics with Environmental Transmission and Reinfection","authors":"T.O. Oluyo ,&nbsp;S.O. Olanrewaju ,&nbsp;V.O. Akinsola ,&nbsp;M.O. Adeyemi ,&nbsp;J.K. Oladejo ,&nbsp;O.A. Odebiyi ,&nbsp;O.A. Adepoju ,&nbsp;A.J. Taiwo","doi":"10.1016/j.fraope.2025.100254","DOIUrl":"10.1016/j.fraope.2025.100254","url":null,"abstract":"<div><div>A deterministic model with a variable human population, rodent population, and Lassa virus in the environment is presented and rigorously analyzed.</div><div>The model analysis showed a process known as backward bifurcation where the Lassa fever-free equilibrium (Disease-free) coexists with Lassa fever present (Endemic equilibrium point) when the threshold parameter <span><math><msub><mrow><mi>R</mi></mrow><mrow><mi>c</mi></mrow></msub></math></span> is below one. The existence resulted from humans who had earlier recovered from Lassa fever being infected again with the Lassa virus when exposed continuously to the virus through environmental sources, close contact with infected individuals, and infected rodents. This result means, that having the threshold parameter <span><math><msub><mrow><mi>R</mi></mrow><mrow><mi>c</mi></mrow></msub></math></span> below one does not guarantee total eradication of the menace.</div><div>Further investigation showed that backward bifurcation could be eliminated in the absence of reinfection. As a result, the global stability of the disease-free equilibrium is guaranteed when the threshold parameter <span><math><msub><mrow><mi>R</mi></mrow><mrow><mi>c</mi></mrow></msub></math></span> is below unity.</div><div>Moreover, using a quadratic Lyapunov function, it is discovered that the unique endemic equilibrium is globally asymptotically stable.</div><div>Numerical analysis revealed the impacts of reinfection and other important parameters on the transmission of the disease. The analysis not only gave a thorough knowledge of the transmission but also justified the analytical results.</div></div>","PeriodicalId":100554,"journal":{"name":"Franklin Open","volume":"11 ","pages":"Article 100254"},"PeriodicalIF":0.0,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143807254","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Retrieval of Chaotic structure along with P-test to Truncated M-fractional Paraxial wave model","authors":"Rashida Hussain,&nbsp;Tayyiaba Rasool,&nbsp;Asghar Ali,&nbsp;Sara Javed","doi":"10.1016/j.fraope.2025.100251","DOIUrl":"10.1016/j.fraope.2025.100251","url":null,"abstract":"<div><div>The Painlevé analysis has attracted considerable attention in the age of mathematical and physical sciences as a crucial method for analyzing FNLPDEs. This methodology is an effective way to assess if a given differential equation ensures the Painlevé property. The concept of the Painlevé property and its connection to integrability has profoundly understood several dynamical systems, including nonlinear processes and classical and quantum mechanics. This work aims to investigate the fundamental ideas behind the Painlevé test, its applications in several scientific fields, and its role in bifurcation and chaos theory in the study of dynamical system behavior. Understanding difficult mathematical and physical concepts, highlighting its function as a cornerstone in modern scientific research.</div></div>","PeriodicalId":100554,"journal":{"name":"Franklin Open","volume":"11 ","pages":"Article 100251"},"PeriodicalIF":0.0,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143828884","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mathematical model for prediction of Tuberculosis in Nigeria using hybrid fractional differential equations and artificial neural network methods","authors":"Samson Linus Manu,&nbsp;Shikaa Samuel,&nbsp;Taparki Richard,&nbsp;Eshi Priebe Dovi","doi":"10.1016/j.fraope.2025.100248","DOIUrl":"10.1016/j.fraope.2025.100248","url":null,"abstract":"<div><div>In this paper, we developed a hybrid Fractional Order Differential Equation (FODE) and Artificial Neural Network (ANN) Model to study the transmission dynamics of Tuberculosis (TB) in Nigeria. The data used for the analysis were obtained from the TB report by the World Health Organisation (WHO) TB Data Base, Nigeria Dash Board, from the years 2010–-2020. For the comparative analysis in this work, two approaches were presented: a system of four FODEs for the TB model formulated in the Caputo sense, and the Hybrid FODE-ANN framework. These FODEs were discretized, and the parameter values were numerically estimated using the Grünwald-Letnikov method while the Hybrid FODE-ANN framework features a NN architecture with one input layer, 15 hidden layers of 100 neurons each, and a hyperbolic tangent (tanh) activation function. Training of the NN involves minimizing a loss function combining data fit and system constraints, optimized using the Adam and L-BFGS algorithms, achieving a high degree of accuracy with an MSE of 6.005×10⁻⁶. The result of FODEs shows an R-square estimation accuracy of 0.9968 but was not sufficiently reliable for predictions. Key findings using the Hybrid FODE-ANN framework reveal a steady decline in the susceptible population, reflecting continuous exposure to TB, and an increasing transmission rate, as estimated by the model. Predictions for the exposed, infected, and recovered compartments fit the observed data, with notable exponential growth in infections and recoveries post-2020. Fractional-order parameters, dynamically estimated during training, demonstrate the dynamical behaviour of TB progression under the hybrid framework. These results highlight the urgent need for enhanced TB control measures in Nigeria, including scaled-up vaccination programs, early diagnosis, isolation protocols, public health awareness, and targeted interventions for high-risk groups.</div></div>","PeriodicalId":100554,"journal":{"name":"Franklin Open","volume":"11 ","pages":"Article 100248"},"PeriodicalIF":0.0,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143814998","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Securing the internet of things: A comprehensive review of ransomware attacks, detection, countermeasures, and future prospects","authors":"Peizhi Yan,&nbsp;Tala Talaei Khoei","doi":"10.1016/j.fraope.2025.100256","DOIUrl":"10.1016/j.fraope.2025.100256","url":null,"abstract":"<div><div>Ransomware attacks present a critical and persistent threat by denying access to data until a ransom is paid. Although existing research on IoT ransomware often concentrates on specific attack types or industry sectors, it frequently overlooks newly emerging threats and lacks holistic defense frameworks applicable across diverse IoT ecosystems. To bridge these gaps, this paper provides a comprehensive analysis of the evolving ransomware landscape targeting IoT devices. We systematically review and categorize detection technologies—ranging from signature-based methods to cutting-edge AI-driven solutions—and assess their effectiveness in mitigating these threats. Additionally, we propose a multi-layered defensive strategy integrating technological, legal, and policy measures to address the complexities of ransomware in IoT settings. Looking ahead, our study highlights potential research directions such as advancing real-time detection, leveraging blockchain for enhanced security and fostering cross-sector collaboration to bolster collective threat intelligence. By emphasizing the importance of a unified approach that involves researchers, industry professionals, and policymakers, we underline the critical steps necessary to fortify IoT infrastructures against the ever-evolving ransomware threat.</div></div>","PeriodicalId":100554,"journal":{"name":"Franklin Open","volume":"11 ","pages":"Article 100256"},"PeriodicalIF":0.0,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143814999","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Illuminant estimation method based on Color Lines and dichroic reflection model","authors":"Soshun Muto ,&nbsp;Mashiho Mukaida ,&nbsp;Noriaki Suetake","doi":"10.1016/j.fraope.2025.100247","DOIUrl":"10.1016/j.fraope.2025.100247","url":null,"abstract":"<div><div>In some shooting environments, a color cast can be introduced by the color of the illuminant. This color cast can result in an image that differs from the original color of the subject, adversely affecting image analysis and recognition processes that rely on accurate color information. White balancing is a technique to eliminate the effects of illuminant and the associated color cast. The effectiveness of white balancing depends on an accurate estimation of the illuminant. Various methods for illuminant estimation have been proposed, including hypothesis based approaches, deep learning methods, and methods based on the dichroic reflection model. However, these methods are insufficient to remove the color cast for the image with a distorted color distribution and/or noise present. In this paper, we propose an illuminant estimation method using Color Lines in conjunction with the dichroic reflection model. In the proposed method, first, specular reflection is calculated based on the dichroic reflection model using a thresholding process to eliminate halation. Subsequently, clustering is applied to the calculated specular reflectance to segment the image into regions affected and unaffected by the illuminant. Meanwhile, it has been reported that Color Lines, which represent the color distribution within local regions of the same object as straight lines, intersect near the color of the illuminant. These intersections are used to identify the region most affected by the illuminant from the clustered specular reflections, which is then estimated as the final illuminant. In the experiments, the effectiveness of the proposed method is verified through both subjective and quantitative comparisons with conventional methods.</div></div>","PeriodicalId":100554,"journal":{"name":"Franklin Open","volume":"11 ","pages":"Article 100247"},"PeriodicalIF":0.0,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143747776","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}